Deep Learning-Based Alzheimer’s Disease Classification: An Experimental Study

Abstract

Alzheimer's disease poses a significant challenge to healthcare professionals due to its prevalence in dementia cases. Accurate and timely diagnosis is essential for effective management of patients. Magnetic resonance imaging (MRI) has emerged as a vital tool for diagnosing Alzheimer's disease. This paper evaluates the effectiveness of image classification models in detecting Alzheimer's disease using MRI images, with four categories of Alzheimer's disease ranging from no dementia to very mild, mild, and moderate dementia. The study employs and fine-tunes different CNN-based model including VGG16, Inception, and ResNetV2. In order to ensure greater reliability and robustness of the results obtained, we employ cross-validation during the experimentation phase, with different test splits. The experimental results demonstrate that fine-tuning VGG16 yields the highest accuracy of 98.810%. These findings suggest that further optimization and refinement of these models may lead to enhanced accuracy in MRI-based Alzheimer's disease diagnosis, potentially revolutionizing how this condition is managed.